Manufacturing Process Control Research Articles

Predictive Modeling and Non-linear Optimization Techniques for Composite Materials Design

With the rise in the use of composite materials for product design, research has been performed in determining the optimal way to produce materials for given desired outputs. As of now response surface methodology and the Taguchi method are the front running methods for optimizing material production methods at the design level. This research investigates why these methods are not a one size fits all solution to optimizing composite materials production for material properties. It proposes utilizing predictive modeling and non-linear optimization techniques from historical manufacturing data of a non-highly controlled manufacturing process. The method is examined with the manufacturing and testing data of a local concrete product manufacturer. The models and optimization methods are validated with residual values to the true data and sensitivity analysis of the problem. The initial testing of the method offers promise to companies who have not found Taguchi or surface response methodology, applicable to their specific business solutions.

National Blood Foundation 2021 Research and Development summit: Discovery, innovation, and challenges in advancing blood and biotherapies.

National Blood Foundation 2021 Research and Development summit: Discovery, innovation, and challenges in advancing blood and biotherapies.

Event-Based PID Control of a Flexible Manufacturing Process

In most cases, the system control is made in a sampled manner, measuring the controlled value at a predefined frequency given by the sampling time. However, not all processes provide relevant information at regular intervals, especially in manufacturing. To reduce the costs and complexity of systems, event-based measuring is necessary. To control this kind of process, an event-based controller is needed. This poses some challenges, especially between the event-triggered measurement, as the process runs in an open loop. In the literature, most event-based controllers are based on the comparison of the error value with a predefined value and activate the controller if this value is crossed. However, in this type of controller, the measured value is measured at a predefine interval and is not suited for most event-based processes. In manufacturing systems, the most usual event-based process is represented by the conveyor transportation system. In this process, the product position is measured only in key locations on the conveyor. For the optimal operation of a flexible manufacturing system, the presence of a product in a key location at predetermined intervals is necessary. For this purpose, this article presents an event-based PID controller implemented on a conveyor transportation system.

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model

The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images.

Cognitive engine for augmented human decision-making in manufacturing process control

A pre-planned production workflow may need to adapt to instantaneous states of resources, materials, and workpieces. This adaptation poses an additional challenge when multiple custom workpieces are enmeshed in complex processes. This paper presents a novel framework, Cognitive Engine Process Controller (CEPC), which realizes a human-centric manufacturing system by augmenting the human in workflow selection and providing the flexibility in using alternative methods in the workflow. The CEPC streamlines overwhelming information for the human to select the next manufacturing steps via dynamic web ontology language (OWL) ontologies, a conflict checker, and a learner. The CEPC can monitor the completion of each step, determine the effect of a failed step, and suggest a set of suitable steps. Simulation results showed the potential of the CEPC via demonstrations. The CEPC has also been compared with an existing cognitive engine framework for workflow planning.

Different Effects of Impurities on Recovered Cathode Materials from Hydrometallurgical Recycling

In the recycling field of lithium-ion batteries (LIB), the hydrometallurgical method has attracted lots of attention in recent years due to its high productivity, low cost and wide applicability. Tremendous effort has been made to improve design and control of manufacturing process to enable a stable and high-quality output. It is clear that the impurities introduced during the recycling process could pose a major impact on the final recovered materials in a variety of aspects. In particular, there are three types of impurities remained in the leaching solution. First, cations such as Al3+, Cu2+ and Fe3+ which come from dissolved metal debris, battery cases or current collectors for instance. Second, anions like F-, S2- and PO4 3- which are introduced from electrode materials or electrolyte salts. Lastly, neutral insoluble substances, for example, carbon particles that are added during the manufacturing process. In our work, systematic investigation has been carried out to explore the influence of different impurities on the recovered NCM622 cathodes via hydrometallurgical approach. It is found that different types of impurities have a unique impact on the cathode properties, and, even extraordinary when they are at certain concentration level.In general, cations have obvious negative impacts on cathode morphology and powder density because of the reaction with OH- in alkaline environment which interferes with the nucleation and growth of precursors. Ultimately, it is very likely to obtain cathodes with irregular particle shapes, reduced sizes and poor tap densities under a high concentration of cation impurities. In these aspects, on the contrary, anions and insoluble substances cause little side effects on the recovered materials. It is noted that the presence of some anions during the co-precipitation, fluoride ions for example, could promote the formation of holes inside particles which can be beneficial to the electrochemical performance of the cathodes.No crystallinity damage or heterogeneous element distribution have been confirmed within as-prepared cathode materials, indicating that a limited range of impurity (maximum 5 at% in our work) could not affect the overall periodicity and consistency in the materials. However, extra phases could be detected in some cathodes when impurity level reaches 5 at%. The excess amount of cation impurities prevents uniform precipitation, resulting in the creation of extra phase Li2MnO3 in the cathodes. Besides, a special extra phase of Li3PO4 exists under the presence of PO4 3- ions due to the reaction with lithium salt during sintering. Other impurities such as carbon and fluorine do not show similar mechanism. Extra phases in the cathodes will trigger a considerable deterioration in the electrochemical performance which needs to be avoided. Thus, special attention is required when impurity concentration exceeds 1 at% level, especially for cations.According to the analysis, some impurity elements could merge into the cathode lattice. Cations like Al3+ and Cu2+ form metal hydroxides during the precursor synthesis, then they are incorporated into the cathodes by occupying the sites of transition metals. A small amount of replacement (< 1 at%), on Ni2+ in particular, could inhibit the cation mixing in the cathodes which results in an improved material quality. In addition, fluoride ion inclusion occurs by the substitution for oxygen in cathodes. This phenomenon not only lowers the average oxidation state of Ni ions, but also raises the lithium diffusivity in the cathodes which significantly increases the cathode capacity and rate performance. In contrast, impurities like carbon nano-particles and PO4 3- do not have clear interactions with the cathode lattice.At 1 at% impurity level, NCM622-Cu shows the highest discharge capacity of 170 mAh/g after 100 cycles at 0.33C (1C = 175 mAh/g). Under the positive influence of fluorine, the cathode NCM622-F also presents a high discharge capacity of 167 mAh/g after 100 cycles at 0.33C which is about 8% better than that of virgin. In terms of Al3+ impurity, 1 at% of NCM622-Al still maintains a similar performance that is comparable to the virgin counterpart. However, 1 at% of carbon impurity has a clear adverse effect on the cathode performance and such a level of ion impurities leads to an even worse deterioration in the properties of recovered NCM622-Fe.To sum up, the characteristics of recovered cathodes via hydrometallurgical process could achieve different levels of variation, either positive or negative at one particular aspect, due to the effects from impurity within certain concentration range. Thus, it is important to figure out the connections and trends behind these factors as well as the possible outcomes that may obtain to enable proper adjustments of the recycling scheme. Figure 1

Tunable soft–stiff hybridized fiber-reinforced thermoplastic composites using controllable multimaterial additive manufacturing technology

As a revolutionary manufacturing technology, material extrusion exhibits great potential for fabricating thermoplastic composites. However, the application of controllable multiple-thermoplastic materials for producing a tunable soft–stiff hybridized fiber-reinforced thermoplastic composite (CCFRTP-TSSH) based on material extrusion has yet to be extensively studied. Thus, this study proposes a controllable multimaterial additive manufacturing process for integrating continuous carbon fibers (CCF) as reinforcement for the thermoplastic structures. Structural reinforcement and tunable soft–stiff hybridization were achieved by adopting thermoplastic polyurethane (TPU) and polylactic acid (PLA) as the soft and stiff materials, respectively. Additionally, the mechanical properties of the CCFRTP-TSSH specimens were systematically investigated and discussed based on various process parameters, such as nozzle temperature, layer thickness, and supporting resin volume ratio (SRVR). The experimental results revealed that the layer thickness had a significant influence on the tensile properties, followed by the SRVR and nozzle temperature, wherein the highest tensile strength obtained was 90.89 MPa. Moreover, the bending strength depends on both the layer thickness and loading direction, with the largest bending strength of 65.96 MPa. Furthermore, the influence of SRVR on various fusion interfaces, e.g., PLA/ TPU, PLA/CCF, and PLA/PLA, was analyzed using scanning electron microscopy, including the influence of process parameters on the wall thickness of the stiff and soft resins, to realize an enhanced tunable soft–stiff hybridization. Ultimately, several distinct components, including a thin-walled hollow cylinder, round table, and a socket for a limb prosthetic, were fabricated to demonstrate the feasibility and accuracy of the proposed method. Therefore, this study presents a novel approach for the integrated manufacturing of soft–stiff hybridized composites, particularly in the field of wearable devices.

GMP Compliant Production of a Cryopreserved Adipose-Derived Stromal Cell Product for Feasible and Allogeneic Clinical Use.

The emerging field of advanced therapy medicinal products (ATMP) holds promise of treating a variety of diseases. Adipose-derived stromal cells (ASCs) are currently being marketed or tested as cell-based therapies in numerous clinical trials. To ensure safety and efficacy of treatments, high-quality products must be manufactured. A good manufacturing practice (GMP) compliant and consistent manufacturing process including validated quality control methods is critical. Product design and formulation are equally important to ensure clinical feasibility. Here, we present a GMP-compliant, xeno-free, and semiautomated manufacturing process and quality controls, used for large-scale production of a cryopreserved off-the-shelf ASC product and tested in several phase I and II allogeneic clinical applications.

An Investigation of Artificial Life to Examine Systems Related to Natural Life, Its Processes, And Its Evolution, Through the Use of Simulations with Computer Models, Robotics, and Biochemistry

This article is based on the CPS or cyber-physical system or intelligent system. The term “physical” is associated with natural and also human man-made systems that have been governed by the laws that are connected with physics while operating in continuity. CPS is described as the network of some embedded systems which establishes an interaction with both physical inputs as well as output. CPS faces the challenges of cyber and physical threats as these systems have been found to be of heterogeneous nature that has a reliance on both private and sensitive data. This study has focused on the CPS and CPS components as well. This is the integration of sensing, controlling, networking and computation into some physical objects as well as infrastructures that connect them through the help of the internet with each other. There are some CPS components that have been utilized for sensing different types of information where the CPS components can be classified into Sensing Components (SC) and also Controlling Components (CC). this study also shed light on the “Programmable Logic Controllers” (PLC) have been created for replacing hard-wired relays and have been assumed in the form of “industrial digital computers” controlling manufacturing processes including the performance of robotic performance. This study has also shed light on the improvements in unsupervised intrusion detection within CPS. Intrusion Detection Systems (IDSs) have been becoming certain building blocks for developing the CPS while detecting its potential threats and also triggering the modules to block and also mitigate those adverse impacts from cyber threats. Development via digital twin through CPS. The CPS paradigm has been involved with physical and software components that have been deeply intertwined with each other where each component has been operating on both spatial and temporal scales possessing multiple behavioural modalities and also interacting with each other. Keywords : CPS systems, intelligent algorithms running, intelligent interfaces, open architecture system CPS, RFID- based position detection.

An Evaluation of Cyber Physical System (CPS) or Intelligent System in Which a Mechanism is Controlled or Monitored by Computer Based Algorithms

This article is based on the CPS or cyber-physical system or intelligent system. The term “physical” is associated with natural and also human man-made systems that have been governed by the laws that are connected with physics while operating in continuity. CPS is described as the network of some embedded systems which establishes an interaction with both physical inputs as well as output. CPS faces the challenges of cyber and physical threats as these systems have been found to be of heterogeneous nature that has a reliance on both private and sensitive data. This study has focused on the CPS and CPS components as well. This is the integration of sensing, controlling, networking and computation into some physical objects as well as infrastructures that connect them through the help of the internet with each other. There are some CPS components that have been utilized for sensing different types of information where the CPS components can be classified into Sensing Components (SC) and also Controlling Components (CC). this study also shed light on the “Programmable Logic Controllers” (PLC) have been created for replacing hard-wired relays and have been assumed in the form of “industrial digital computers” controlling manufacturing processes including the performance of robotic performance. This study has also shed light on the improvements in unsupervised intrusion detection within CPS. Intrusion Detection Systems (IDSs) have been becoming certain building blocks for developing the CPS while detecting its potential threats and also triggering the modules to block and also mitigate those adverse impacts from cyber threats. Development via digital twin through CPS. The CPS paradigm has been involved with physical and software components that have been deeply intertwined with each other where each component has been operating on both spatial and temporal scales possessing multiple behavioural modalities and also interacting with each other. Keywords : CPS systems, intelligent algorithms running, intelligent interfaces, open architecture system CPS, RFID- based position detection.

Probabilistic Digital Twin for Additive Manufacturing Process Design and Control

Abstract This paper proposes a detailed methodology for constructing an additive manufacturing (AM) digital twin for the laser powder bed fusion (LPBF) process. An important aspect of the proposed digital twin is the incorporation of model uncertainty and process variability. A virtual representation of the LPBF process is first constructed using a physics-based model. To enable faster computation required in uncertainty analysis and decision-making, the physics-based model is replaced by a cheaper surrogate model. A two-step surrogate model is proposed when the quantity of interest is not directly observable during manufacturing. The data collected from the monitoring sensors are used for diagnosis (of current part quality) and passed on to the virtual representation for model updating. The model updating consists of Bayesian calibration of the uncertain parameters and the discrepancy term representing the model prediction error. The resulting digital twin is thus tailored for the particular individual part being produced and is used for probabilistic process parameter optimization (initial, before starting the printing) and online, real-time adjustment of the LPBF process parameters, in order to control the porosity in the manufactured part. A robust design optimization formulation is used to minimize the mean and standard deviation of the difference between the target porosity and the predicted porosity. The proposed methodology includes validation of the digital twin in two stages. Validation of the initial model in the digital twin is performed using available data, whereas data collected during manufacturing are used to validate the overall digital twin.

TEMPO-Functionalized Carbon Nanotubes for Solid-Contact Ion-Selective Electrodes with Largely Improved Potential Reproducibility and Stability.

Solid-contact ion-selective electrodes (SCISEs) can overcome essential limitations of their counterparts based on liquid contacts. However, attaining a highly reproducible and predictable E0, especially between different fabrication batches, turned out to be difficult even with the most established solid-contact materials, i.e., conducting polymers and large-surface-area conducting materials (e.g., carbon nanotubes), that otherwise possess excellent potential stability. An appropriate batch-to-batch E0 reproducibility of SCISEs besides aiding the rapid quality control of the electrode manufacturing process is at the core of their “calibration-free” application, which is perhaps the last major challenge for their routine use as single-use “disposable” or wearable potentiometric sensors. Therefore, here, we propose a new class of solid-contact material based on the covalent functionalization of multiwalled carbon nanotubes (MWCNTs) with a chemically stable redox molecule, (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO). This material combines the advantages of (i) the large double-layer capacitance of MWCNT layers, (ii) the adjustable redox couple ratio provided by the TEMPO moiety, (iii) the covalent confinement of the redox couple, and (iv) the hydrophobicity of the components to achieve the potential reproducibility and stability for demanding applications. The TEMPO-MWCNT-based SC potassium ion-selective electrodes (K+-SCISEs) showed excellent analytical performance and potential stability with no sign of an aqueous layer formation beneath the ion-selective membrane nor sensitivity toward O2, CO2, and light. A major convenience of the fabrication procedure is the E0 adjustment of the K+-SCISEs by the polarization of the TEMPO-MWCNT suspension prior to its use as solid contact. While most E0 reproducibility studies are limited to a single fabrication batch of SCISEs, the use of prepolarized TEMPO-MWCNT resulted also in an outstanding batch-to-batch potential reproducibility. We were also able to overcome the hydration-related potential drifts for the use of SCISEs without prior conditioning and to feature application for accurate K+ measurements in undiluted blood serum.

The integrated application of acceptance sampling in the quality control of manufacturing process and quality assurance of shipment product

In this work, the authors propose the integrated application of CSP-1 plan and Dodge-Romig rectifying inspection plan with expected inspection cost, expected replacement cost, expected warranty cost, expected quality loss for conforming product, and expected quality loss for non-conforming product per item. The quality characteristics of product is considered as the normal distribution with nominal-the-best and symmetric quadratic quality loss function. The CSP-1 plan is firstly applied in the process control. The optimal combination of parameters is obtained from the minimum expected total cost per item produced under the specified average outgoing quality limit (AOQL) protection. One needs to have the optimal clearance number, sampling frequency, and specification limit coefficient. Then Dodge-Romig rectifying inspection plan is applied in the product improvement. The optimal sample size, clearance number, and quality investment are obtained by minimizing the expected total cost per lot under the specified consumer’s risk.

Research on the application of 22Na radiolocation detection technology in advanced manufacturing process control

Abstract This article mainly studies the positioning function of radioactive detection technology in process control for processing devices. The accuracy of 22Na detection is not limited by the spatial area by comparing different illumination scenarios; the accuracy of inspection is independent of the accuracy of machining equipment; the accuracy of the detection is not affected by the conditions of the processed body. This study is of great significance for the future radioactive detection technology to make up for the lack of precision caused by the existing sensor technology on the spatial positioning of the processing device, the illumination environment and the material characteristics of the processed body, and for the process control research in the field of advanced manufacturing.

Decision-based virtual metrology for advanced process control to empower smart production and an empirical study for semiconductor manufacturing

Virtual metrology (VM) has been employed to improve the performance of advanced process control for semiconductor manufacturing. A number of VM models have been proposed to predict the quality characteristics for the wafers that have not been sampled and measured. However, little research has been done to address the interrelations between the VM model and associated decisions for advanced process control and yield enhancement. There is a research need for developing a framework that can integrate the confidence level of VM prediction and domain knowledge to derive appropriate decisions for real-time control. To fill the gaps, this study aims to develop a decision-based virtual metrology framework that integrates clustering and regression models to enhance the prediction and ensure the decision quality for the R2R controller. In particular, Isolation Forest is employed to cluster the data group for multi-recipes and multi-tools. Random Forest Regression is developed for the prediction model for each category respectively to enhance the accuracy of predicted results. Furthermore, this approach designs an overall confidence score based on data integrity and predicted results to suggest the optimal decision rules for R2R control in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. Indeed, the results have demonstrated practical viability and the developed solution has been implemented.

Predictive evaluation of multicomponent direct compress model tablets by integrating sphere UV-Vis spectroscopy and chemometrics.

Control of the pharmaceutical manufacturing process and active pharmaceutical ingredients (API) is essential to product formulation and bioavailability. The aim of this study is to predict tablet surface API concentration by chemometrics using integrating sphere UV-Vis spectroscopy, a non-destructive and contact-free measurement method. Riboflavin, pyridoxine hydrochloride, dicalcium phosphate anhydrate, and magnesium stearate were mixed and ground with a mortar and pestle, and 100mg samples were subjected to direct compression at a compaction pressure of 6MPa at 7mm diameter. The flat surface tablets were then analyzed by integrating sphere UV-Vis spectrometry. Standard normal variate (SNV) normalization and principal component analysis were applied to evaluate the measured spectral dataset. The spectral ranges were prepared at 300-800nm and 500-700nm with SNV normalization. Partial least squares (PLS) regression models were constructed to predict the API concentrations based on two previous datasets. The regression vector of constructed PLS regression models for each API was evaluated. API concentration prediction depends on riboflavin absorbance at 550nm and the excipient dicalcium phosphate anhydrate. Integrating sphere UV-Vis spectrometry is a useful tool to process analytical technology.

A Smart Digital Twin to Stabilize Return Sand Temperature without Using Coolers

In order to ensure the optimal state of recovered molding sand inside a foundry, it is necessary to avoid temperature peaks and to ensure optimal humidity conditions prior to reusing the sand. Sand that is too hot or without optimal moisture can cause production delays due to a long mixing process, excessive consumption of raw materials, or poor agglutination. To ensure a stable and optimal sand temperature, many foundries choose to incorporate coolers into their process, however, it is a solution that is not always viable, either due to their high cost or a lack of space within the facility. Another solution is to incorporate water sprinklers into the cooling drum which contribute by reducing the temperature of the castings and the sand, but these systems do not prevent temperature peaks from occurring. Therefore, here, we present a control methodology, based on a digital architecture that, governed by an intelligent digital twin allows us to know the real situation and the current rate of production, providing suggestions for water addition. The obtained system reduces the average temperature and its variation, as well as eliminates temperature peaks, giving a more controlled manufacturing process.

Key wastes selection and prediction improvement for biogas production through hybrid machine learning methods

As history shows, a linear production process results in a waste of resources. Using renewable energy can not only reduce waste but also protect the earth's resources. However, due to the high input costs of biogas manufacturing and a lack of standard manufacturing procedures, there have been few predictive models generated using multiple algorithms to analyze key input wastes and to predict output in the field of biogas generation. The lack of key wastes analysis may result in reductions in both the optimization of production processes and the stability of production, eventually affecting the popularity of biogas generation. Therefore, this research proposes a hybrid machine learning method based on random forest (RF) and long short-term memory (LSTM) analysis for redefining the key factors and improving the prediction of biogas generation output. The analysis adopted input and production output data from an existing biogas plant. Regarding the predication for biogas production, the performance of the proposed hybrid model exhibits 20% more accuracy than its nearest competitor among traditional analytic models. Coupled with the assistance of a hybrid machine learning method, the identification of key factors can be improved, providing users with suggestions and guidance for modifying parameters. Through a precise control of final output and manufacturing process, this methodology may be expected to help reduce costs and accelerate the popularity of biogas.

Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives

Today's manufacturing processes are pushed to their limits to generate products with ever-increasing quality at low costs. A prominent hurdle on this path arises from the multiscale, multiphysics, dynamic, and stochastic nature of many manufacturing systems, which motivated many innovations at the intersection of artificial intelligence (AI), data analytics, and manufacturing sciences. This study reviews recent advances in Mechanistic-AI, defined as a methodology that combines the raw mathematical power of AI methods with mechanism-driven principles and engineering insights. Mechanistic-AI solutions are systematically analyzed for three aspects of manufacturing processes, i.e., modeling, design, and control, with a focus on approaches that can improve data requirements, generalizability, explainability, and capability to handle challenging and heterogeneous manufacturing data. Additionally, we introduce a corpus of cutting-edge Mechanistic-AI methods that have shown to be very promising in other scientific fields but yet to be applied in manufacturing. Finally, gaps in the knowledge and under-explored research directions are identified, such as lack of incorporating manufacturing constraints into AI methods, lack of uncertainty analysis, and limited reproducibility and established benchmarks. This paper shows the immense potential of the Mechanistic-AI to address new problems in manufacturing systems and is expected to drive further advancements in manufacturing and related fields.

Six-Sigma Quality Improvement Approach to Continuous Loop Management

Six Sigma is a quality management method for identifying and eliminating defects and helping companies improve current processes, products, or services. The aim is to streamline the quality control of manufacturing and business processes so that there are no major differences throughout the operation. This paper describes a management model that emerges by overlaying Nonaka's knowledge creation theory over decision-making and value creation processes borrowed from six-sigma quality improvement practices. The resulting Continuous Loop Model (CLM) illustrates how these Japanese and Western concepts and practices work together in harmony to increase the rate of knowledge creation through continuous feedback loops making the firm more agile, adaptable, innovative, and valuable. The results of this study provide empirical data suggesting that firms that successfully cultivate cultures that are strong in fairness, credibility, respect (collectively trust), pride, and camaraderie significantly outperform comparable firms in several key areas; value, operating effectiveness, and growth.